{"id":"W2111806784","doi":"10.1109/deec.2005.25","title":"Using semantic information to improve transparent query caching for dynamic content Web sites","year":2005,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Query optimization; Query expansion; Sargable; Cache; Web search query; Web query classification; Information retrieval; Benchmark (surveying); Query language; Database; Spatial query; Dynamic web page; World Wide Web; Search engine; Web page; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004935528,0.001633164,0.001791898,0.001994433,0.001304851,0.004022908,0.004325971,0.001385359,0.001234883],"category_scores_gemma":[0.02763801,0.0009760286,0.0009154328,0.003021478,0.001537241,0.01123651,0.003071054,0.001767374,0.0004935516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002133526,"about_ca_system_score_gemma":0.003722594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01315047,"about_ca_topic_score_gemma":0.01010888,"domain_scores_codex":[0.9933016,0.00143936,0.0006919943,0.0006092548,0.002890383,0.00106739],"domain_scores_gemma":[0.9777017,0.009361133,0.00178843,0.006716492,0.003753455,0.0006788328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00397238,0.001684649,0.0501486,0.001071668,0.0005887331,0.001150841,0.002307239,0.3365174,0.2054514,0.02176063,0.01196818,0.3633783],"study_design_scores_gemma":[0.0001054859,0.0004095497,0.002770538,0.00003456035,0.0001802989,0.0003176647,0.0002273621,0.9371715,0.04958283,0.004269768,0.004822073,0.0001083248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4651125,0.003667341,0.4977243,0.0008775372,0.0001987438,0.0004193231,0.0004411949,0.02735045,0.004208656],"genre_scores_gemma":[0.8852324,0.0004662998,0.1115783,0.0001603857,0.00007724108,0.00007275162,0.0007362039,0.0007089524,0.0009675152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01315047,"threshold_uncertainty_score":0.0261479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05944628871965589,"score_gpt":0.2803099796013074,"score_spread":0.2208636908816515,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}